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Fundamentals of Analytics > Module 2 > Flashcards

Module 2 Flashcards

Business and Organization Skills, Technical Skills, Workplace Skills

1
Q

How many skills are under Analytics Competencies?

A

Four

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2
Q

Four Categorized Skills of Analytics Competencies

A

Business and Organization Skills
Technical Skills
Workplace Skills

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3
Q

How many distinct competencies does Business and Organization Skills have?

A

Four

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4
Q

Four Distinct Competencies of Business and Organization Skills.

A
  • Domain Knowledge and Application
  • Data Management and Governance
  • Operational Analytics
  • Data Visualization and Presentation
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5
Q

How many distinct competencies does Technical Skills have?

A

Five

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6
Q

Five Distinct Competencies of Technical Skills.

A
  • Research Methods
  • Data Engineering Principles
  • Statistical Techniques
  • Data Analytics, Methods, and Algorithms
  • Computing
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7
Q

How many distinct competencies does Workplace Skills have?

A

One

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8
Q

How many Competencies are there overall?

A

Ten Competencies (Categorized into Four)

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9
Q

T or F

every competencies has three level
proficiency expectations.

A

True

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10
Q

What are the three levels of proficiency?

A

Entry Level
Immediate Level
Expert Level

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11
Q

What does Entry level proficiency do?

A

Perform tasks with Guidance

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12
Q

What does Immediate level proficiency do?

A

Formulate task to achieve Organizational Goals
and works independently.

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13
Q

What does Expert level proficiency do?

A

Identifying new approaches to achieve
Organizational Goals. Provides solution to a problem.

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14
Q

In the domain of Knowledge Application and Domain Expertise, one must have the following skills:

A
  • Domain-Related Knowledge
  • Insights to effectively contextualize data
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15
Q

These skills defined a Functional Analyst, it encompasses what?

A

industry knowledge, business experience, and domain expertise.

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16
Q

Comprehend the collected data, and grasp the methods by which they are managed and applied within the specific industry domain

A

Entry Level Domain Knowledge and Application

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17
Q

Craft a comprehensive content strategy and design an effective information
architecture tailored to support the unique needs of a given industry domain and
its diverse audiences.

A

Immediate Level Domain Knowledge and Application

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18
Q

Formulate compelling business cases aimed at enhancing domain-related
procedures by leveraging data-driven decision-making strategies.

A

Expert Level Domain Knowledge and Application

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19
Q

Competency needed for Functional Analysts

A

Domain Knowledge and Application

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20
Q

Competency needed for Data Stewards

A

Data Management and Governance

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21
Q

In the domain of Data Management and Governance, one must have the following skills:

A
  • Develop and Implement Data Management Strategies
  • Enforcing Privacy and Data Security
  • Implement Data Policies and Regulations
  • Understand Ethical Considerations
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22
Q

They are the Data
Gatekeepers of an organization.

A

Data Stewards

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23
Q
  • Maintain vigilant awareness and consistently implement policies and measures to
    uphold data security, privacy, intellectual property, and ethical standards.
A

Entry Level Data Management and Governance

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24
Q

Competency needed for Analytics Managers

A

Operational Analytics

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24
Q

In the domain of Operational Analytics, one must have the
following skills:

A
  • General Knowledge of Business Analytics
  • Specialized Knowledge of Business Techniques
  • Insight Derivation for Decision-Making.
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24
Q
  • Effectively implement and enforce policies and procedures pertaining to data
    security, privacy, intellectual property, and ethical considerations.
A

Immediate Level Data Management and Governance

25
Q
  • Formulate comprehensive policies addressing data security, privacy, intellectual
    property, and ethical considerations.
A

Expert Level Data Management and Governance

25
Q

These skills defined an Analytics Manager as they have what skills?

A

Project Management Skills.

26
Q

Conduct comprehensive business analysis on designated tasks and datasets.

A

Entry Level Operational Analytics

27
Q

Determine the business implications arising

A

Immediate Level Operational Analytics

28
Q

Discover fresh opportunities to leverage historical data for optimizing
organizational processes.

A

Expert Level Operational Analytics

29
Q

In the domain of Data Visualization and Presentation, one must have the following skills:

A
  • Create and Communicate Compelling and Actionable Insights
  • Utilizing Data Visualization and Presentation
30
Q

These data visualization techniques are not just about charts but about telling a story

A

Data-Storytelling

31
Q
  • Create data visualization reports or narratives according to specified requirements.
A

Entry Level Data Visualization and Presentation

32
Q
  • Design infographics to facilitate the effective presentation and communication of
    actionable outcomes.
A

Immediate Level Data Visualization and Presentation

33
Q
  • Choose suitable visualization methods and innovate new approaches tailored to a
    specific industry.
A

Expert Level Data Visualization and Presentation

34
Q

In the domain of Research Methods, one must have the following skills:

A
  • Utilize scientific and engineering methods
  • Discover and create new knowledge and insights
35
Q

These skills defined a Data Scientist, it encompasses what?

A

strategies, processes, and techniques.

36
Q
  • Employ the 4-step research model, comprising hypothesis formulation, research
    methods selection, artifact creation, and evaluation, to enhance understanding
    and application in research endeavors.
A

Entry Level Research Methods

37
Q
  • Formulate research questions centered on identified issues within established
    research or business process models.
A

Immediate Level Research Methods

38
Q
  • Create experiments incorporating both passive and active data collection methods
    to facilitate hypothesis testing and effective problem-solving.
A

Expert Level Research Methods

39
Q

In the domain of Data Engineering Principles, one must have the following skills:

A
  • Utilize software and system engineering
  • Develop data analytics application
40
Q

These skills defined a Data Engineer, it encompasses what?

A

ETL Method (Extract, Transform, Load).

41
Q

Proficiency in programming selected SQL and NoSQL platforms for data storage
and access, with a specific focus on writing Extract, Transform, Load (ETL) scripts.

A

Entry Level Data Engineering

42
Q
  • Architect and construct both relational and non-relational databases, ensuring the
    implementation of efficient Extract, Transform, Load (ETL) processes tailored for
    large datasets.
A

Immediate Level Data Engineering

43
Q
  • Demonstrated advanced expertise in leveraging modern Big Data technologies for
    processing diverse data types sourced from multiple channels.
A

Expert Level Data Engineering

44
Q

In the domain of Statistical Techniques, one must have the following skills:

A
  • Apply Statistical Concepts and Methodologies for data analysis
45
Q

They are utilized to analyze raw data especially from a research data to extract information.

A

Mathematics and Statistics

46
Q

Under Statistical Techniques these skills are also defined by Data Scientist, it
encompasses what

A

Mathematics and Statistics

47
Q

Possess proficiency in employing statistical methods, including sampling, ANOVA,
hypothesis testing, descriptive statistics, regression analysis, and other relevant
methodologies.

A

Entry Level Statistical Techniques

48
Q
  • Evaluate and recommend the most suitable statistical methods and tools tailored
    to specific tasks and datasets.
A

Immediate Level Statistical Techniques

49
Q
  • Recognize issues within collected data and propose corrective measures,
    encompassing additional data collection, inspection, and pre-processing as
    needed.
A

Expert Level Statistical Techniques

50
Q

In the domain of Data Analytics, Method and Algorithms, one must have the following skills:

A
  • Implement and Evaluate Machine Learning Methods and Algorithms
  • Deriving Insights from data for Decision-Making.
51
Q

Under Data Analytics, Methods and Algorithms these skills are also defined by Data Scientist, it
encompasses what?

A

Algorithm and Machine Learning.

52
Q

They are utilized to identify the most appropriate methods or algorithms to extract insights from data.

A

Algorithm and Machine Learning.

53
Q
  • Illustrate comprehension of statistical hypothesis testing and proficiently conduct
    such tests, providing clear explanations regarding the statistical significance of
    collected data.
A

Entry Level Data Analytics, Methods and Algorithms

54
Q
  • Apply quantitative techniques, such as time series analysis, optimization, and
    simulation, to deploy suitable models for analysis and prediction.
A

Immediate Level Data Analytics, Methods and Algorithms

55
Q
  • Evaluate data reliability and appropriateness. Choose suitable approaches while
    considering their impact on analysis and the quality of results.
A

Expert Level Data Analytics, Methods and Algorithms

56
Q

In the domain of Computing, one must have the following skills:

A
  • Apply information technology and computational thinking.
  • Utilize programming languages for analysis.
  • Utilize software and hardware solutions also for analysis.
56
Q
  • Conduct fundamental data manipulation, analysis, and visualization tasks
    proficiently.
A

Entry Level Computing

57
Q
  • Utilize computational thinking to translate formal data models and algorithmic
    processes into program code.
A

Immediate Level Computing

58
Q
  • Choose suitable application and statistical programming languages, as well as
    development platforms, tailored to specific processes and datasets.
A

Expert Level Computing

59
Q

There’s no really three level skill set as this is a necessary skill for
any type of field. The following essential skills are included but
not limited to:

A
  • Critical Thinking
  • Communication
  • Collaboration
  • Creativity and Attitude
  • Planning and Organizing
  • Business Fundamentals
  • Customer Focus
  • Working with Tools and
    Technology
  • Dynamic (Self-) Re-Skilling
  • Professional Network
  • Ethics

Fundamentals of Analytics (3 decks)

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